基于Jackson网络的地面分队防御作战兵力部署方法
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陆军装甲兵学院

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E920.8

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Forces deployment method of ground unit defensive combat based on Jackson network
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Army Academy of Armored Forces

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    摘要:

    针对地面分队防御作战兵力部署信息利用不足、未能宏观统筹部署的问题,提出一种基于Jackson网络的地面分队防御作战兵力部署方法,实现对地面分队防御作战兵力部署的建模和优化。首先,分析地面分队防御敌突击武器的规律和特点,用Jackson排队网络对该过程建模,并针对第一道防线节点顾客到达率大于服务率的问题,提出Jackson排队网络的等效转化方法;其次,依据各节点的吞吐量和顾客到达率,研究节点间的顾客转移量、外来顾客到达率,并最终求得敌目标对防线的突破率。最后,基于博弈思想,建立双层迭代寻优结构,计算敌最优进攻策略下我方的最优决策。仿真实验验证了本文改进人工蜂群算法在求解本文模型收敛性和稳定性方面的优势以及本文双层迭代寻优结构的合理性,同时讨论了毁伤概率对兵力部署的影响。

    Abstract:

    In order to solve the problems of insufficient information utilization and macro overall deployment of ground unit"s defensive combat forces, a method of ground unit"s defensive combat forces deployment based on Jackson network was proposed to realize the modeling and optimization of ground unit"s defensive combat forces deployment.Firstly, the law and characteristics of the ground detachment"s defense against enemy assault weapons are analyzed, and the Jackson queuing network is used to model it. Aiming at the problem that the customer arrival rate is greater than the service rate, the equivalent transformation method of the Jackson queuing network is proposed.Secondly, according to the throughput and customer arrival rate of each node, the volume of customer transfer and external customer arrival rate between nodes are studied, and finally the penetration rate of the enemy target to the defense line is obtained.Finally, based on the game theory, a two-level iterative optimization structure is established to calculate the optimal decision of our party under the optimal attack strategy of the enemy.Simulation experiments verify the advantages of the improved artificial bee colony algorithm in solving the convergence and stability of the proposed model and the rationality of the two-layer iterative optimization structure. Meanwhile, the influence of damage probability on the decision of military deployment is discussed.

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  • 收稿日期:2021-04-11
  • 最后修改日期:2021-09-13
  • 录用日期:2021-09-22
  • 在线发布日期: 2021-10-01
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